F1 Score is a performance metric used in supervised learning tasks, such as binary or multi-class classification. The F1 score combines precision and recall into one metric. It is calculated by taking the harmonic mean of precision and recall for the given data set.
The precision of the system is defined as the ratio of true positives to the total number of instances predicted to be positive (TP + FP). On the other hand, the recall of the system is defined as the ratio of true positives to the total number of instances that were truly positive (TP + FN). Therefore, the F1 score is the harmonic mean of precision and recall.
The F1 score is particularly important when the data set is imbalanced, meaning that there is a disproportionate number of either positive or negative instances. In these cases, precision and recall are not sufficient to determine the performance of a classifier. The F1 score is then used to provide an overall average balance between them.
Moreover, the F1 score is commonly used in modern machine learning models, as well as in medical diagnostic tools. In these instances, it is applied to determine if the model is able to classify the correct instances or not.
In conclusion, the F1 score is a performance metric used to evaluate the performance of a supervised learning task, such as classification. It is a combination of precision and recall and it is typically used in cases where data sets are imbalanced.
